Li Taole, Guo Jifeng
Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.
Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Centre for Medical Genetics, Central South University, Changsha, China.
PLoS One. 2024 Dec 31;19(12):e0316179. doi: 10.1371/journal.pone.0316179. eCollection 2024.
Parkinson's disease (PD) is a complex neurodegenerative disease with unclear pathogenesis. Some recent studies have shown that there is a close relationship between PD and ferroptosis. We aimed to identify the ferroptosis-related genes (FRGs) and construct competing endogenous RNA (ceRNA) networks to further assess the pathogenesis of PD.
Expression of 97 substantia nigra (SN) samples were obtained and intersected with FRGs. Bioinformatics analysis, including the gene set enrichment analysis (GSEA), consensus cluster analysis, weight gene co-expression network analysis (WGCNA), and machine learning algorithms, were employed to assess the feasible differentially expressed genes (DEGs). Characteristic signature genes were used to create novel diagnostic models and construct competing endogenous RNA (ceRNA) regulatory network for PD, which were further verified by in vitro experiments and single-cell RNA sequencing (scRNA-seq).
A total of 453 DEGs were identified and 11 FRGs were selected. We sorted the entire PD cohort into two subtypes based on the FRGs and obtained 67 hub genes. According to the five machine algorithms, 4 features (S100A2, GNGT1, NEUROD4, FCN2) were screened and used to create a PD diagnostic model. Corresponding miRNAs and lncRNAs were predicted to construct a ceRNA network. The scRNA-seq and experimental results showed that the signature model had a certain diagnostic effect and lncRNA NEAT1 might regulate the progression of ferroptosis in PD via the NEAT1/miR-26b-5p/S100A2 axis.
The diagnostic signatures based on the four FRGs had certain diagnostic and individual effects. NEAT1/miR-26b-5p/S100A2 axis is associated with ferroptosis in the pathogenesis of PD. Our findings provide new solutions for treating PD.
帕金森病（PD）是一种发病机制不明的复杂神经退行性疾病。最近的一些研究表明，PD与铁死亡之间存在密切关系。我们旨在鉴定铁死亡相关基因（FRGs）并构建竞争性内源RNA（ceRNA）网络，以进一步评估PD的发病机制。
获取97个黑质（SN）样本的表达数据，并与FRGs进行交集分析。采用生物信息学分析，包括基因集富集分析（GSEA）、共识聚类分析、加权基因共表达网络分析（WGCNA）和机器学习算法，来评估可行的差异表达基因（DEGs）。使用特征性标志基因创建新的诊断模型，并构建PD的竞争性内源RNA（ceRNA）调控网络，通过体外实验和单细胞RNA测序（scRNA-seq）进一步验证。
共鉴定出453个DEGs，筛选出11个FRGs。基于FRGs将整个PD队列分为两个亚型，获得67个枢纽基因。根据五种机器学习算法，筛选出4个特征（S100A2、GNGT1、NEUROD4、FCN2）并用于创建PD诊断模型。预测相应的miRNA和lncRNA构建ceRNA网络。scRNA-seq和实验结果表明，标志模型具有一定的诊断作用，lncRNA NEAT1可能通过NEAT1/miR-26b-5p/S100A2轴调节PD中铁死亡的进展。
基于四个FRGs的诊断标志具有一定的诊断和个体化作用。NEAT1/miR-26b-5p/S100A2轴在PD发病机制中与铁死亡相关。我们的研究结果为治疗PD提供了新的解决方案。
